
Hexagon will showcase its high-precision manufacturing and digital quality control solutions at the SIMTOS 2026 exhibition in Seoul, South Korea, scheduled for April 13-17, 2026.
Software
Originally reported by epnc.co.kr
Hexagon will showcase its high-precision manufacturing and digital quality control solutions at the SIMTOS 2026 exhibition in Seoul, South Korea, scheduled for April 13-17, 2026. The company will feature the Maestro high-speed digital 3D coordinate measuring machine, the ATS800 laser tracker for large-scale structural inspection, and the Proplan AI software designed to reduce CAM programming time by up to 75 percent. Steffen Dilger, President of the Production Software division, will lead the company's presence, focusing on the integration of manufacturing data with autonomous inspection workflows to improve production efficiency in aerospace, automotive, and wind energy sectors.
Hexagon's participation highlights the ongoing convergence of metrology and additive manufacturing, where real-time data feedback loops are essential for maintaining quality in complex, high-precision production environments. By integrating AI-driven CAM tools and automated inspection systems with autonomous mobile robots, the company addresses the industry-wide challenge of reducing manual bottlenecks in post-processing and quality assurance. This approach competes directly with integrated manufacturing platforms from Siemens, Dassault Systemes, and Zeiss, which are similarly pushing for closed-loop digital twins to minimize scrap rates in high-value manufacturing.
For manufacturers, the focus on Proplan AI and automated inspection systems like the ATS800 represents a practical step toward reducing the labor-intensive nature of CAM programming and large-part verification. Users should evaluate how these specific software and hardware integrations fit into their existing digital thread to ensure compatibility with current CNC and additive workflows. Success for Hexagon in this market segment will depend on the scalability of these AI tools across diverse, non-standardized production environments.
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